The use of digitized multimedia, such as motion video and still images, has increased the demand on microprocessors and available bandwidth. The use of World Wide Web browsers as graphical user interfaces and electronic commerce on the Internet has increased the need for graphical images that are visually appealing and are of high resolution. Unfortunately, high image quality creates a demand for increased storage space for digital data. Industry has recognized the need for compression of the digital data to help reduce the problem. Compression is a process intended to yield a compact digital representation of a signal. In the cases where the signal is defined as an image, the problem of compression is to minimize the number of bits needed to digitally represent the image. There are many applications that benefit when image signals are available in compressed form, such as digital photography, electronic commerce, digital video processing and archiving digital images for on-line catalogs. Without compression, most digital information and transmission through the normal limited bandwidth channels is difficult or infeasible for practical use. For example, consider the case of facsimile transmission. Typically, an 8.5×11 inch page is scanned and digitized at 300 dots per inch, thus resulting in 8.5×11×300×300−8415000 bits. Transmitting this data via a low-cost 14.4 Kbps modem could require 9.74 minutes.
Compression of digital data is the process of representing this data using as few bits or bytes as possible. Generally, there exist two types of image compression for digital data—lossless and lossy. Lossless compression does not lose any data in the compression and allows image data to be stored using less memory, such as Random Access Memory (RAM), than an uncompressed image with the ability to restore the original data exactly. Lossy compression further reduces the amount of needed memory, but does not guarantee exact restoration of the original data. Existing technology for lossless compression, however, may not allow for high compression ratios. If the electronic data signal is an image, the differences between the original and the lossy compressed restoration may not even be visually noticeable for low compression levels using existing compression technology.
There exist several lossy compression methods available for image compression. The Joint Photographic Experts Group (JPEG) has provided one of the most popular still image compression technologies available today. Other file formats include PCX, GIF and BMP. The IS 10918-1 (ITU-T.81) standard for image compression is the result of JPEG and is usually referred to as the JPEG standard. This standard combines the discrete cosine transform (DCT) and extensive Huffman tables to produce a compression algorithm. Since JPEG has an underlying DCT based technology, it operates on eight by eight blocks of pixels. Although JPEG is popular, one known problem is that, as the compression level increases, the image quality worsens and distortion grows in these blocks individually. This problem leads to a block effect that introduces edges into the image, which is normally detected as jagged edges or maybe a blurring to the observer of the decompressed image. Because storage space or bandwidth may be limited, additional space or bandwidth is costly or unavailable, high compression, (such as 100:1), is generally preferred than lower compression of data
Since the development of digital signal processing in the early 1980's, a digital form of wavelet transform called Discrete Wavelet Transform (DWT) has become an important tool for image processing and image compression. DWT is a lossless transform, which is used to form an orthonomal basis of some, and a dilated master function over a range of, shift and dilation parameters. The principle behind the wavelet transform is to hierarchically or recursively decompose the input signals into a series of successively lower resolution reference signals and their associated detail signals. At each level, the reference signals and detailed signals contain the information needed for reconstruction back to the next higher resolution level. Onedimensional DWT (1-D DWT) processing can be described in terms of a Finite Impulse Response (FIR) filter bank, wherein an input signal is analyzed in both low and high frequency subbands.
A separable two-dimensional DWT process is a straightforward extension of 1-D DWT. Specifically, in the 2-D DWT process, separable filter banks are applied first horizontally and then vertically. Referring to FIG. 1, the 2-D DWT process decomposes an image into a reference signal, and three detail signals. The signals from the filter bank comprising two filters, first horizontally then vertically, gives rise to an analysis in four frequency subbands: subband LL, horizontal low-vertical low as known as a reference signal; subband LH, horizontal low-vertical high; subband HL, horizontal high-vertical low; and subband HH, horizontal high-vertical high. Each resulting band is encoded according to its unique information content for transmission from a coding station to a receiving station. Constraints, however, exist on how filters can be designed an/or selected, including the need to output perfect reconstructions, the finite-length of the filters and a regularity requirement that the iterated low pass filters involve convergence to continuous functions.
Thus what is needed is a method and apparatus of compressing and decompressing image data that overcomes the problems in prior art. There is also a need to perform high compression of data and at the same time redisplay the underlying image at high visual quality.